Many vehicles today are increasingly aware of objects and traffic infrastructure in their vicinity. Road-users, such as pedestrians, bicyclists, and other vehicles, are detected by one or more on-board sensors in the vehicle, and their positions relative to the vehicle are determined. On-board sensors in the vehicle are also used to estimate the geometry of the road on which the vehicle is currently travelling. Control systems comprised in the vehicle then make use of this position and geometry data in, e.g., active safety applications or for autonomous drive of the vehicle. Some vehicles today also use wireless communications equipment for exchanging information with other road-users, and with the surrounding traffic infrastructure, in order to obtain further data related to the vehicle surroundings.
A wide variety of different techniques for estimating positions of road-users and for estimating road geometry exist in literature;
US 2012/0271483 discloses a method and apparatus for recognizing the shape of a road partly based on a detected object on the road. The disclosure relates to determining road geometry and also to determining a probability that other objects on the road are located in the same lane as the vehicle in which the method is implemented.
WO 2012/104918 teaches a road shape estimation device for estimation of road shape in a vehicle. The estimation of road shape is based on detecting an angle and position of another vehicle in front of the vehicle in which the device is implemented.
US 2010/0250064 discloses using lane markings and other objects on the road, as well as the motion of the own vehicle, in order to estimate road geometry.
Thus, there have been disclosed methods and devices for estimating road geometry in a vehicle. However, having access to an estimate of road geometry is not sufficient for many applications. The quality of the estimated road geometry is often also necessary to know.
Herein, the grade of knowledge about the quality of an estimated road geometry is referred to as the confidence level of the estimated road geometry. The confidence level of an estimate provides information about an expected error magnitude of the estimate. Thus, the accuracy of an estimated road geometry associated with a high confidence level is largely known, e.g., as accurate or as inaccurate, whereas an estimated road geometry associated with a low confidence level is not known to be either accurate or inaccurate, since the grade of knowledge about the quality of the estimated road geometry is low.
For instance, in an autonomous drive application the confidence level of an estimated road geometry is needed in order to decide when autonomous drive of the vehicle can be performed in a secure manner, and when autonomous drive should be terminated due to that the estimated road geometry cannot be trusted, either because the confidence level is high and the expected error magnitude in the estimate is known to be high, or because the confidence level is low such that the expected error magnitude is largely unknown.
Consequently, there is a need for methods for determining a confidence level of an estimated road geometry. These methods for determining confidence levels of estimated road geometries should be reliable, and preferably of low complexity, in order to enable efficient implementation.